One problem for understanding action selection is determining the level of abstraction used for specifying an "act". At the most basic level of abstraction, an atomic act could be anything from contracting a muscle cell to provoking a war. Typically for any one action-selection mechanism, the set of possible actions is predefined and fixed.

Most researchers working in this field place high demands on their agents:

The main problem for action selection is complexity. Since all computation takes both time and space (in memory), agents cannot possibly consider every option available to them at every instant in time. Consequently, they must be biased, and constrain their search in some way. For AI, the question of action selection is what is the best way to constrain this search? For biology and ethology, the question is how do various types of animals constrain their search? Do all animals use the same approaches? Why do they use the ones they do?

One fundamental question about action selection is whether it is really a problem at all for an agent, or whether it is just a description of an emergent property of an intelligent agent's behavior. However, if we consider how we are going to build an intelligent agent, then it becomes apparent there must be some mechanism for action selection. This mechanism may be highly distributed (as in the case of distributed organisms such as social insect colonies or slime mold) or it may be a special-purpose module.

The action selection mechanism (ASM) determines not only the agent’s actions in terms of impact on the world, but also directs its perceptual attention, and updates its memory. These egocentric sorts of actions may in turn result in modifying the agents basic behavioural capacities, particularly in that updating memory implies some form of machine learning is possible. Ideally, action selection itself should also be able to learn and adapt, but there are many problems of combinatorial complexity and computational tractability that may require restricting the search space for learning.

In AI, an ASM is also sometimes either referred to as an agent architecture or thought of as a substantial part of one.

Generally, artificial action selection mechanisms can be divided into several categories: symbol-based systems sometimes known as classical planning, distributed solutions, and reactive or dynamic planning. Some approaches do not fall neatly into any one of these categories. Others are really more about providing scientific models than practical AI control; these last are described further in the next section.

Early in the history of artificial intelligence, it was assumed that the best way for an agent to choose what to do next would be to compute a probably optimal plan, and then execute that plan. This led to the physical symbol system hypothesis, that a physical agent that can manipulate symbols is necessary and sufficient for intelligence. Many software agents still use this approach for action selection. It normally requires describing all sensor readings, the world, all of ones actions and all of one's goals in some form of predicate logic. Critics of this approach complain that it is too slow for real-time planning and that, despite the proofs, it is still unlikely to produce optimal plans because reducing descriptions of reality to logic is a process prone to errors.

Satisficing is a decision-making strategy which attempts to meet criteria for adequacy, rather than identify an optimal solution. A satisficing strategy may often, in fact, be (near) optimal if the costs of the decision-making process itself, such as the cost of obtaining complete information, are considered in the outcome calculus.

Goal driven architectures - In these symbolic architectures, the agent's behaviour is typically described by a set of goals. Each goal can be achieved by a process or an activity, which is described by a prescripted plan. The agent must just decide which process to carry on to accomplish a given goal. The plan can expand to subgoals, which makes the process slightly recursive. Technically, more or less, the plans exploits condition-rules. These architectures are reactive or hybrid. Classical examples of goal driven architectures are implementable refinements of Belief-Desire-Intention architecture like JAM or IVE.

In contrast to the symbolic approach, distributed systems of action selection actually have no one "box" in the agent which decides the next action. At least in their idealized form, distributed systems have many modules running in parallel and determining the best action based on local expertise. In these idealized systems, overall coherence is expected to emerge somehow, possibly through careful design of the interacting components. This approach is often inspired by neural networks research. In practice, there is almost always some centralised system determining which module is "the most active" or has the most salience. There is evidence real biological brains also have such executive decision systems which evaluate which of the competing systems deserves the most attention, or more properly, has its desired actions disinhibited.

ASMO is an attention-based architecture developed by Rony Novianto. It orchestrates a diversity of modular distributed processes that can use their own representations and techniques to perceive the environment, process information, plan actions and propose actions to perform.

Various types of winner-take-all architectures, in which the single selected action takes full control of the motor system

Extended Rosenblatt & Payton is a spreading activation architecture developed by Toby Tyrrell in 1993. The agent's behaviour is stored in the form of a hierarchical connectionism network, which Tyrrell named free-flow hierarchy. Recently exploited for example by de Sevin & Thalmann (2005) or Kadleček (2001).

Behavior based AI, was a response to the slow speed of robots using symbolic action selection techniques. In this form, separate modules respond to different stimuli and generate their own responses. In the original form, the subsumption architecture, these consisted of different layers which could monitor and suppress each other's inputs and outputs.

Because purely distributed systems are difficult to construct, many researchers have turned to using explicit hard-coded plans to determine the priorities of their system.

Dynamic or reactive planning methods compute just one next action in every instant based on the current context and pre-scripted plans. In contrast to classical planning methods, reactive or dynamic approaches do not suffer combinatorial explosion. On the other hand, they are sometimes seen as too rigid to be considered strong AI, since the plans are coded in advance. At the same time, natural intelligence can be rigid in some contexts although it is fluid and able to adapt in others.

Other structured reactive plans tend to look a little more like conventional plans, often with ways to represent hierarchical and sequential structure. Some, such as PRS's 'acts', have support for partial plans.[1] Many agent architectures from the mid-1990s included such plans as a "middle layer" that provided organization for low-level behavior modules while being directed by a higher level real-time planner. Despite this supposed interoperability with automated planners, most structured reactive plans are hand coded (Bryson 2001, ch. 3). Examples of structured reactive plans include James Firby's RAP System and the Nils Nilsson's Teleo-reactive plans. PRS, RAPs & TRP are no longer developed or supported. One still-active (as of 2006) descendent of this approach is the Parallel-rooted Ordered Slip-stack Hierarchical (or POSH) action selection system, which is a part of Joanna Bryson's Behaviour Oriented Design.

Sometimes to attempt to address the perceived inflexibility of dynamic planning, hybrid techniques are used. In these, a more conventional AI planning system searches for new plans when the agent has spare time, and updates the dynamic plan library when it finds good solutions. The important aspect of any such system is that when the agent needs to select an action, some solution exists that can be used immediately (see further anytime algorithm).

CogniTAO is a decision making engine it based on BDI (Belief desire intention), it includes built in teamwork capabilities.

Soar is a symboliccognitive architecture. It is based on condition-action rules known as productions. Programmers can use the Soar development toolkit for building both reactive and planning agents, or any compromise between these two extremes.

ACT-R is similar to Soar. It includes a Bayesian learning system to help prioritize the productions.

Many dynamic models of artificial action selection were originally inspired by research in ethology. In particular, Konrad Lorenz and Nikolaas Tinbergen provided the idea of an innate releasing mechanism to explain instinctive behaviors (fixed action patterns). Influenced by the ideas of William McDougall, Lorenz developed this into a "psychohydraulic" model of the motivation of behavior. In ethology, these ideas were influential in the 1960s, but they are now regarded as outdated because of their use of an energy flow metaphor; the nervous system and the control of behavior are now normally treated as involving information transmission rather than energy flow. Dynamic plans and neural networks are more similar to information transmission, while spreading activation is more similar to the diffuse control of emotional / hormonal systems.